Facilitating panoramic video streaming with brain-computer interactions

ABSTRACT

Aspects of the subject disclosure may include, for example, obtaining one or more signals, the one or more signals being based upon brain activity of a viewer while the viewer is viewing media content; predicting, based upon the one or more signals, a first predicted desired viewport of the viewer; obtaining head movement data associated with the media content; predicting, based upon the head movement data, a second predicted desired viewport of the viewer; comparing the first predicted desired viewport to the second predicted desired viewport, resulting in a comparison; and determining, based upon the comparison, to use the first predicted desired viewport to facilitate obtaining a first subsequent portion of the media content or to use the second predicted desired viewport to facilitate obtaining a second subsequent portion of the media content. Other embodiments are disclosed.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/244,059 filed on Apr. 29, 2021, which is a continuation of U.S.patent application Ser. No. 16/513,780 filed on Jul. 17, 2019 (now U.S.Pat. No. 11,025,981). The contents of each of the foregoing is/arehereby incorporated by reference into this application as if set forthherein in full.

FIELD OF THE DISCLOSURE

The subject disclosure relates to facilitating panoramic video streamingwith brain-computer interactions.

BACKGROUND

Panoramic or immersive video (each of which can include, for example,360-degree video), is a critical component in the Virtual Reality (“VR”)ecosystem. Such 360-degree video (sometimes referred to herein as 360°video) is becoming increasingly popular on commercial video contentplatforms. In a typical 360-degree video system, a user wearing a VRheadset can freely change his or her viewing direction. Technically, theuser is situated in the center of a virtual sphere, and the panoramiccontent downloaded from video server(s) is projected onto the sphere(e.g., using equirectangular projection). The user's viewport (visiblearea) is typically determined by his or her viewing direction (inlatitude/longitude) and the Field of View (“FoV”) of the VR headset inreal-time. The FoV defines the extent of the observable area, which isusually a fixed parameter of a VR headset. As shown in FIG. 2A, a userwearing a VR headset 201 can adjust his or her orientation by changingthe pitch, yaw, and roll, which correspond to rotating along the X, Y,and Z axes, respectively (see, also, in this FIG. example viewport 203).

Maintaining good Quality of Experience (“QoE”) for 360° videos overbandwidth-limited links on commodity mobile devices remains challenging.First, 360° videos are large: under the same perceived quality, 360°videos have around 5×larger sizes than conventional videos. Second, 360°videos are complex: sophisticated projection and content representationschemes may incur high overhead. Third, 360° videos are stillunder-explored: there is a lack of real-world experimental studies ofkey aspects such as rate adaptation, QoE metrics, and cross-layerinteractions (e.g., with TCP and web protocols such as HTTP/2).

Certain existing work on 360° video streaming can be divided into twocategories: monolithic streaming and tile-based streaming. A simplymonolithic streaming delivers uniformly encoded panoramic views and iswidely used by most commercial 360° video content providers. Thedrawback is that, at any given time, typically only 15-20% of thecontent that is being downloaded is actually in the FoV of the end user,which is a waste of bandwidth resources. For more advanced schemes thatperform viewport adaptation, a 360° video has multiple versions eachhaving a different scene region, sometimes called a Quality EmphasizedRegion (“QER”), with a high encoding rate. A video player picks thecorrect version based on the viewer's head orientation. This scheme issometimes referred to as versioning-based 360° video streaming. Onepractical issue of this versioning-based 360° video streaming scheme isthat it incurs significant overhead at the server side (e.g., theFACEBOOK OCULUS 360 mechanism is believed to require servers to maintainup to 88 versions of the same video).

For the tiling scheme, a 360° video is spatially segmented into tiles.Delivered are mainly tiles overlapping with predicted FoVs forviewport-adaptive video streaming. To increase the robustness, a videoplayer can also fetch the remaining tiles at lower qualities. Each 360°video chunk is pre-segmented into multiple smaller chunks, which arecalled tiles. The easiest way to generate the tiles is to evenly dividea chunk containing projected raw frames into m×n rectangles eachcorresponding to a tile. Suppose, for example, that the projectedvisible area is θ. In this example, the client (e.g., video player) onlysends requests for the tiles that overlap with θ.

Referring now to FIG. 2B, an example is shown where m=6 and n=4, and θis the shaded oval region 210. An original video chunk is segmented intotiles. A tile (see, e.g., the tile in the upper right-hand corner) hasthe same duration and number of frames as the chunk it belongs to, butoccupies only a small spatial portion. Each tile can be independentlydownloaded and decoded. A tile can also refer to a small spatial portiononly in a frame. In that sense, a tile-based video chunk can beindependently fetched and decoded. In this example, the client will onlyrequest the six tiles overlapping with θ (that is, where 1≤x≤3, 1≤y≤2).Note that due to projection, despite the viewer's FoV being fixed, thesize of θ and thus the number of requested tiles may vary. Compared toFoV-agnostic approaches, tiling offers significant bandwidth savings.Also note that the tiling scheme can be applied to not only videos usingEquirectangular projection, but also those with Cube Map projection.

Certain proposals have previously been made to improve the accuracy ofviewport prediction by leveraging data fusion of multiple sources, suchas head movement, video content analysis and user profile. Popular 360°videos from commercial content providers and video-sharing websitesattract a large number of viewers (e.g., more than 4 million views ofthe video represented by FIG. 2B). Also, it is known that users' viewingbehaviors are often affected by the video content. It is believed thatthis is also true for 360° videos: at certain scenes, viewers are morelikely to look at certain spots or directions, and thus the FoV can bepredicted based on the video content. Consider an example of a mountainclimbing video. When viewers are “climbing” towards the peak, they maylook upward most of the time to figure out how long it will take toreach the peak.

Based on the above observation, there have been proposals to usecrowdsourced viewing statistics by instrumenting the 360° video playersto record the frequency of a given FoV, which can be easily be collectedby video servers. With the crowdsources data, a heat map can begenerated showing the most frequently viewed content in a 360° video. Inthe literature, viewing statistics have been leveraged to estimate thevideo abandonment rate and to automatically rate video contents. In thecontext of 360° videos, for each chunk, the server can also recorddownload frequencies of its tiles, and provide client video players withsuch statistics as a heat map of content access pattern through metadataexchange. A tile's download frequency is defined in this context as thenumber of video sessions that fetch this tile divided by the totalnumber of sessions accessing this video.

Besides the heat map based approach, certain proposals have previouslybeen made to employ object-feature detection for certain types ofvideos. For example, for soccer and tennis videos, these objects couldbe the soccer and tennis balls, key soccer players and referee. Whenwatching these sport videos, most likely the viewers will follow themovement of the soccer and tennis balls. Even without using the heatmap, it can be predicted that the tiles containing the ball will verylikely overlap with the FoV and these tiles can be identified viaobject-feature detection of video frames.

Moreover, certain existing work has demonstrated that it is possible tomodel the video viewing behavior of users by leveraging stochasticmodels such as a Markovian model. The model can be constructed usingactions from a user when viewing a 360° video, including pause, stop,jump, forward and rewind. This type of user profile complements the headmovement based FoV prediction. Even if a user does not change the viewdirection, the FoV may change dramatically if a forward/rewind action isissued by the viewer. The stochastic models of video viewing behaviorcan help improve the accuracy of FoV prediction. The future FoVprediction can also leverage the personal interest of a user. Forexample, if it is known from a profile that a user does not likethrilling scenes, very likely he/she will skip this type of content whenwatching a 360° video. Thus, the probability of predicting a FoV fromthese scenes will be low.

Reference will now be made to certain aspects of conventionalBrain-Computer Interfaces for VR. According to Wikipedia: Abrain-computer interface (BCI), sometimes called a neural-controlinterface (NCI), mind-machine interface (MMI), direct neural interface(DNI), or brain-machine interface (BMI), is a direct communicationpathway between an enhanced or wired brain and an external device.Certain existing BCI mechanisms can be divided into three categories:invasive BCIs that are directly implanted into the grey matter of thebrain; partially invasive BCI devices which are implanted inside theskull with the rest outside the brain and thus the grey matter; andnon-invasive BCIs. The most widely used non-invasive BCIs leverageelectroencephalography (“EEG”), mainly due to its portability, ease ofuse, fine temporal resolution and low set-up cost. However, it issomewhat susceptible to noise. Other technologies that have been usedsuccessfully for non-invasive BCIs include magnetoencephalography(“MEG”) and functional Magnetic Resonance Imaging (“fMRI”).

A number of prototypes have been proposed to enable users to navigate invirtual environments [see, e.g., Doron Friedman, Robert Leeb, ChristophGuger, Anthony Steed, Gert Pfurtscheller and Mel Slater. NavigatingVirtual Reality by Thought: What Is It Like? Presence, Vol. 16, No. 1,pages 100-110, 2007] and manipulate virtual objects [see, e.g., AnatoleLécuyer, Fabien Lotte, Richard B. Reilly, Robert Leeb, Michitaka Hiroseand Mel Slater. Brain-Computer Interfaces, Virtual Reality, andVideogames. Computer, Vol. 41, No. 10, pages 66-72, 2008] solely byBCIs, for example, by analyzing cerebral activity which is recorded onthe scalp via EEG electrodes. In terms of conventional use infacilitating panoramic video viewing experiences, BCIs can be decomposedinto several elementary tasks, such as moving left/right and up/down, inorder to change the viewport when viewing 360° videos. It has actuallybeen shown by Pfurtscheller and Neuper [see, e.g., Gert Pfurtschellerand Christa Neuper. Motor Imagery and Direct Brain-ComputerCommunication. Proceedings of IEEE 82(7), pages 1123-1134] that one canidentify from EEG signals several mental processes, for example, theimagination of various predefined movements. One can then transform suchthought-related EEG signals into a control signal, which can in turn beassociated with a few simple computer commands, such as cursor movement.

Referring now to FIG. 2C, a diagram is depicted that shows how certainconventional BCIs can facilitate various VR applications (includingnavigation in the context of panoramic 360° video streaming).Essentially, there is a closed loop with four steps. First, the EEGdevice 222 collects the “thoughts” from a viewer through the BCI device224 attached on the head of the viewer. Second, the EEG analyzer 226processes the signals and transforms them into navigation commands, suchas (for example) moving toward left. Third, the EEG analyzer 226 sendsthe instructions to the VR display device 228 (which could be, forexample, combined with the BCI device 224 into a single unit). Finally,the VR device 228 changes the viewport based on the instruction from theEEG analyzer 226.

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an example, non-limitingembodiment of a communication network in accordance with various aspectsdescribed herein.

FIG. 2A is a diagram illustrating various conventional coordinatesystems and an FoV example associated with adjusting of 360° videoviewing directions.

FIG. 2B is a diagram illustrating certain conventional example chunk,tile, frame, and tile segmentation (4×6 tiles).

FIG. 2C is a diagram that shows how certain conventional BCIs canfacilitate various VR applications.

FIGS. 2D, 2E, 2F show, respectively, graphs of data (which can beapplicable to various embodiments) related to prediction accuracy ofdifferent Machine Learning algorithms for three prediction windows: 0.2s, 0.5 s and is (each for 4×6 segmentation of tile-based streaming).

FIG. 2G is a block diagram illustrating an example, non-limitingembodiment of a workflow functioning within the communication network ofFIG. 1 in accordance with various aspects described herein (the workflowof this FIG. utilizes BCIs for viewport prediction).

FIG. 2H depicts an illustrative embodiment of a method in accordancewith various aspects described herein.

FIG. 2I depicts an illustrative embodiment of a method in accordancewith various aspects described herein.

FIG. 2J depicts an illustrative embodiment of a method in accordancewith various aspects described herein.

FIG. 2K is a block diagram illustrating an example, non-limitingembodiment of a system in accordance with various aspects describedherein.

FIG. 3 is a block diagram illustrating an example, non-limitingembodiment of a virtualized communication network in accordance withvarious aspects described herein.

FIG. 4 is a block diagram illustrating an example, non-limitingembodiment of a computing environment in accordance with various aspectsdescribed herein.

FIG. 5 is a block diagram illustrating an example, non-limitingembodiment of a mobile network platform in accordance with variousaspects described herein.

FIG. 6 is a block diagram illustrating an example, non-limitingembodiment of a communication device in accordance with various aspectsdescribed herein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments for facilitating panoramic video streaming withbrain-computer interactions. Other embodiments are described in thesubject disclosure.

Various embodiments facilitate and optimize the delivery of 360° videosby leveraging brain-computer interactions (e.g., to improve the accuracyof head movement based viewport prediction). As described herein,certain conventional viewport prediction techniques have enabledviewport adaptive streaming of 360° videos (which delivers onlypredicted viewport of a frame at high quality and the remainder of theframe with a low quality). Such certain conventional work mainlyutilizes viewport movement traces (e.g., by collecting the head movementtraces when viewing 360° videos with a head-mounted display) for theprediction. Although this approach is lightweight and can achieve areasonable accuracy in general, it is typically limited by the inherent“randomness” of the movement trajectory. Further, viewport changes arenaturally controlled by the brain of the viewer and certain conventionalBrain-Computer Interfaces (“BCIs”) have been extensively investigated tofacilitate various aspects of human-computer interactions. On one hand,it has been demonstrated that BCI could potentially provide moreintuitive and suitable interactions for VR applications. On the otherhand, the research community widely accepts that VR could be a promisingmedium for efficiently improving BCI systems. Various embodimentsdescribed herein combine the predictions of future viewport from headmovement traces and viewport moving direction derived from the analysisof brain signals. If the two predictions are consistent, then (in oneembodiment) the fine-granularity prediction from head movement traceswill be used to actively and adaptively prefetch video content in thepredicted viewport in advance. Otherwise (that is, if the twopredictions are not consistent), the viewport video prefetching will (inone embodiment) be guided (e.g., largely guided) by the prediction ofviewport moving direction from brain signals (which, it is believed,should be more accurate than the head movement based viewportprediction).

Referring now to FIG. 1 , a block diagram is shown illustrating anexample, non-limiting embodiment of a communication network 100 inaccordance with various aspects described herein. For example,communication network 100 can facilitate in whole or in part panoramicvideo streaming (such as in the context of viewport prediction/selectionas described herein). In particular, a communications network 125 ispresented for providing broadband access 110 to a plurality of dataterminals 114 via access terminal 112, wireless access 120 to aplurality of mobile devices 124 and vehicle 126 via base station oraccess point 122, voice access 130 to a plurality of telephony devices134, via switching device 132 and/or media access 140 to a plurality ofaudio/video display devices 144 via media terminal 142. In addition,communication network 125 is coupled to one or more content sources 175of audio, video, graphics, text and/or other media. While broadbandaccess 110, wireless access 120, voice access 130 and media access 140are shown separately, one or more of these forms of access can becombined to provide multiple access services to a single client device(e.g., mobile devices 124 can receive media content via media terminal142, data terminal 114 can be provided voice access via switching device132, and so on).

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110,wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 175. The communicationsnetwork 125 can include a circuit switched or packet switched network, avoice over Internet protocol (VoIP) network, Internet protocol (IP)network, a cable network, a passive or active optical network, a 4G, 5G,or higher generation wireless access network, WIMAX network,UltraWideband network, personal area network or other wireless accessnetwork, a broadcast satellite network and/or other communicationsnetwork.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) and/or other access terminal.The data terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G, 5G, or higher generation modem, an optical modem and/orother access devices.

In various embodiments, the base station or access point 122 can includea 4G, 5G, or higher generation base station, an access point thatoperates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers and/or other displaydevices.

In various embodiments, the content sources 175 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and/or othersources of media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc. can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

As described herein, an essential component in certain viewport-adaptive360° video players is to predict users' future viewports (e.g., via headmovement prediction), which is important for both tiling-based andversioning-based viewport-adaptive 360° video streaming. Provided hereinis a discussion directed to a systematic investigation that was made ofreal users' head movements, as well as how to efficiently performviewport prediction on mobile devices. Using a dataset consisting of4420-minute 360° video playback time, studied were a wide spectrum ofmachine learning (“M”L) algorithms for viewport prediction. Also,designed were lightweight but robust viewport prediction methods bystrategically leveraging off-the-shelf ML algorithms.

With reference now to viewport prediction accuracy, it is noted thatideally, if a viewer's future FoV during a 360° video session is knownbeforehand, the optimal sequence of tiles that minimizes the bandwidthconsumption can be generated. By leveraging head movement traces, forexample, a sliding window of 1 second from T−1 to T can be used topredict future head position (and thus the FoV) at T+δ for eachdimension of yaw, pitch, and roll. Evaluated were the predictionaccuracy of various Machine Learning algorithms for three predictionwindows, 0.2, 0.5 and is (see FIGS. 2D, 2E, 2F).

Still referring to FIGS. 2D, 2E, 2F, the training data is historicalhead movement traces collected during the user study mentioned abovewith more than 130 participants. Used were four Machine Learningalgorithms—3 classical models and 1 neural network model. The classicalmodels are Linear Regression, Ridge Regression and Support VectorRegression (with rbf kernel). The neural network model is Multi-LayerPerceptron. Also used was a simple heuristic, called Static, whichassumes that the viewport does not change from T to T+δ. For the 4×6segmentation scheme, the viewport prediction is accurate if the tile setdetermined by the predicted viewport is exactly the same as the groundtruth. The key take-away from FIGS. 2D, 2E, 2F is that the viewportprediction accuracy depends heavily on the prediction window. The longerthis window is, the lower the prediction accuracy. However, smallerprediction windows lead to a strict requirement on the end-to-endlatency.

As described herein in connection with various embodiments, if it can bedetermined in advance how a viewer is going to change the viewport (forexample, how the viewer is going to move his or her head) when watchinga 360° video then this information can be utilized to improve theaccuracy of viewport prediction. In one example, the future headmovement can be predicted by analyzing brain signals/waves of theviewer.

Referring now to FIG. 2G, a diagram (according to an embodiment) ofworkflow that leverages BCIs for improving the accuracy of viewportprediction for 360° video streaming is shown. In this FIG. the workflowincludes step 232, which is collecting and processing the brain signalsof a viewer when he or she watches a 360° video. By analyzing thesesignals (see step 234), it can be determined roughly to which directionthe viewer wants to change his or her viewport. The workflow alsoincludes step 236, which is collecting viewport movement trajectory, forexample, from head movement traces collected by motion sensors. At step238 one or more machine learning technologies are applied to predict oneor more future viewports based on the collected movement traces. At thispoint, there are now two sources of prediction for future viewports (onesource based on BCI and another source based on head movement data). Atstep 240 the two viewport predictions from the two sources are compared(and it is determined whether the two viewport predictions areconsistent with each other).

Still referring to FIG. 2G, if the moving direction analyzed from theBCI signals aligns with the future viewport predicted using machinelearning algorithms (e.g., linear regression), then the workflowprefetches video content in the machine learning predicted (consistent)viewport which usually can provide fine-granularity information (seestep 244 as a result of “YES” from step 240). Otherwise (see step 242 asa result of “NO” from step 240), BCI-based prediction will be used toguide the content prefetch (which, in theory, should be more accuratethan machine learning based prediction). During the user study describedherein, it was found that some viewers will suddenly and dramaticallychange the viewing direction, for example, when attracted by a loudsound from the left while moving their heads toward the right. In thiscase, viewport prediction by applying machine learning algorithms onhead movement traces will become inaccurate, because the predicted trendis no longer valid any more. On the other hand, the BCI-based solutionof various embodiments described herein can offer more accurateprediction by analyzing what the viewer's “thoughts” are. Of note,various embodiments described herein can be agnostic to the underlyingtechnology to support BCIs (which can be, for example, EEG, MEG, and/orfMRI). In another example, an underlying technology to support BCIs canbe any technology that enables the understanding of the mapping betweenbrain signals and the predefined navigation commands.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a number of blocks in FIG. 2G, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

Referring now to FIG. 2H, various steps of a method 2000 according to anembodiment are shown. As seen in this FIG. 2H, step 2002 comprisesobtaining, by a system including a processor, one or more signals, theone or more signals being based upon brain activity of a viewer whilethe viewer is viewing media content. Next, step 2004 comprisespredicting by the system, based upon the one or more signals, a firstpredicted desired viewport of the viewer. Next, step 2006 comprisesobtaining, by the system, head movement data associated with the mediacontent. Next, step 2008 comprises predicting by the system, based uponthe head movement data, a second predicted desired viewport of theviewer. Next, step 2010 comprises comparing, by the system, the firstpredicted desired viewport to the second predicted desired viewport,resulting in a comparison. Next, step 2012 comprises determining by thesystem, based upon the comparison, to use the first predicted desiredviewport to facilitate obtaining a first subsequent portion of the mediacontent or to use the second predicted desired viewport to facilitateobtaining a second subsequent portion of the media content.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 2H, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

Referring now to FIG. 2I, various steps of a method 3000 according to anembodiment are shown. As seen in this FIG. 2I, step 3002 comprisesobtaining from one or more sensors brain activity data that is basedupon brain activity of a viewer, the brain activity data beingassociated with viewing by the viewer of media content. Next, step 3004comprises predicting a first predicted desired viewport of the viewer,the first predicted desired viewport being predicted based upon thebrain activity data. Next, step 3006 comprises obtaining head movementdata associated with the media content. Next, step 3008 comprisespredicting a second predicted desired viewport of the viewer, the secondpredicted desired viewport being based upon the head movement data.Next, step 3010 comprises determining, based upon a comparison of thefirst predicted desired viewport to the second predicted desiredviewport, whether to use the first predicted desired viewport tofacilitate obtaining a first subsequent portion of the media content orto use the second predicted desired viewport to facilitate obtaining asecond subsequent portion of the media content.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 2I, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

Referring now to FIG. 2J, various steps of a method 4000 according to anembodiment are shown. As seen in this FIG. 2J, step 4002 comprisesreceiving from a device a request for a portion of media content, therequest indicating a desired viewport, the request having been made bythe device in accordance with a determination by the device to use asthe desired viewport one of a first predicted desired viewport or asecond predicted desired viewport, the determination having been basedupon a comparison between the first predicted desired viewport and thesecond predicted desired viewport, the first predicted desired viewporthaving been predicted by the device based upon brain activity of aviewer engaged in watching an earlier portion of the media content, andthe second predicted desired viewport having been predicted by thedevice based upon head movement data associated with the earlier portionof the media content. Next, step 4004 comprises sending, to the device,the portion of media content that had been requested.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIG. 2J, itis to be understood and appreciated that the claimed subject matter isnot limited by the order of the blocks, as some blocks may occur indifferent orders and/or concurrently with other blocks from what isdepicted and described herein. Moreover, not all illustrated blocks maybe required to implement the methods described herein.

Referring now to FIG. 2K, depicted is a block diagram illustrating anexample, non-limiting embodiment of a system 250 in accordance withvarious aspects described herein. As seen in this FIG. server(s) 252 arein bi-directional communication with media player 254 via the Internet256. The server(s) 252 store content (e.g., 360° video content) that isstreamed to the media player. The server(s) 252 also store a database ofhistoric head movement data associated with the stored video content.The media player 254 obtains (from BCI 258) BCI data (e.g., real-timeBCI data) associated with a viewer who is using the media player 254 toview a video (the BCI 258 can be separate from the media player 254 orintegrated with/into the media player 254). The media player 254requests from the server(s) 252 appropriate portions of the video (e.g.,appropriate tiles). The appropriate portions can be determined by themedia player 254 using determination techniques as described herein.

In another example, the media player 254 sends to the server(s) 252 theBCI data, the server(s) 252 determine (based upon the BCI data and/orthe historic head movement data) the appropriate portions of the video(e.g., appropriate tiles) to send back to the media player 254, and theserver(s) 252 send back to the media player 254 the determinedappropriate portions of the video (e.g., appropriate tiles). Theappropriate portions can be determined by the servers(s) 252 usingdetermination techniques as described herein.

As described herein, a key basis of certain previous proposals relatedto viewport prediction of 360° videos is the historical head movementtrajectory and the consideration of other factors that may indirectlyaffect the head movement, for example, video content and user profile.However, some of these schemes are typically not very accurate,especially for large prediction windows, due to the inherent randomnessof human head movement when watching 360° videos. Further, how theviewport changes can actually be considered as directly determined bywhat content the viewer wants to consume and thus is controlled by theviewer's brain. Therefore, various embodiments described herein targetan improvement of the viewport prediction accuracy (for example, in thecontext of panoramic video streaming) by leveraging brain-computerinteractions. In one specific example, viewport prediction accuracy isimproved for large prediction windows.

As described herein, in a manner differently from certain existinghead-mounted displays that control the viewport mainly based on motiondata from sensors, when using a BCI according to an embodiment a viewerdoes not need to move his or her head in order to change the contentthey are seeing for 360° video streaming. The viewer can navigate viahis or her “thoughts”.

As described herein, a BCI can be used to navigate the viewing. In anembodiment, even if a BCI is not used to actually control thenavigation, a BCI can be used to leverage the BCI signals to facilitatethe viewport prediction.

As described herein, a comparison can be made between a BCI-basedprediction (based on brain activity) and a traditional viewportprediction (based on head movement trajectory). If they are consistent,the fine-granularity prediction from head movement traces can be used toactively and adaptively prefetch video content in the predicted viewportin advance. Otherwise, the viewport video prefetching can be largelyguided by the prediction of viewport moving direction from brain signals(which should be more accurate than the head movement based viewportprediction).

In one example, the determination that the viewport predicted by thehead movement data should be used can be made based upon the viewportpredicted by the head movement data corresponding to a set of tiles thatmatch on a one-to-one basis with a set of tiles that correspond to theviewport predicted by the brain-computer interface data (wherein, inthis example, if there is not a one-to-one match in tiles the viewportpredicted by the brain-computer interface data would be used instead).

In another example, the determination that the viewport predicted by thehead movement data should be used can be made based upon the viewportpredicted by the head movement data corresponding to a set of tiles thatmatch at least on a percent basis (the percent of this example beingless than 100 percent and greater than 0 percent) with a set of tilesthat correspond to the viewport predicted by the brain-computerinterface data (wherein, in this example, if there is not a match abovea certain threshold percentage basis then the tiles of the viewportpredicted by the brain-computer interface data would be used instead).

As described herein, improvements can be provided to viewport predictionaccuracy and streaming efficiency for 360° videos via brain-computerinteractions. Various embodiments bring one or more of the followingfour key benefits. First, congestion can be alleviated in the cellularcore network by delivering less data for 360° videos through moreaccurate predication and more efficient content caching. Second, thecellular data usage of mobile users can be optimized and the stall timeof video playback can be reduced, thus improving the quality of userexperience. Third, energy consumption on mobile devices can bedecreased, by avoiding transmitting unnecessary data when delivering360° videos. Finally, various embodiments are lightweight and enabletrue spatial immersion by delivering 4K+ quality videos over a networkinfrastructure with limited bandwidth.

In another example, various embodiments can be implemented in thecontext of any type of panoramic or immersive video (e.g. 360° video,180° video).

Referring now to FIG. 3 , a block diagram 300 is shown illustrating anexample, non-limiting embodiment of a virtualized communication networkin accordance with various aspects described herein. In particular avirtualized communication network is presented that can be used toimplement some or all of the subsystems and functions of communicationnetwork 100, the subsystems and functions of system 220, the functionsof workflow 230, method 2000, method 300 and method 400 presented inFIGS. 1, 2C, 2G, 2H, 2I AND 2J. For example, virtualized communicationnetwork 300 can facilitate in whole or in part panoramic video streaming(such as in the context of viewport prediction/selection as describedherein).

In particular, a cloud networking architecture is shown that leveragescloud technologies and supports rapid innovation and scalability via atransport layer 350, a virtualized network function cloud 325 and/or oneor more cloud computing environments 375. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs); reduces complexity fromservices and operations; supports more nimble business models; andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationnetwork employs virtual network elements (VNEs) 330, 332, 334, etc. thatperform some or all of the functions of network elements 150, 152, 154,156, etc. For example, the network architecture can provide a substrateof networking capability, often called Network Function VirtualizationInfrastructure (NFVI) or simply infrastructure that is capable of beingdirected with software and Software Defined Networking (SDN) protocolsto perform a broad variety of network functions and services. Thisinfrastructure can include several types of substrates. The most typicaltype of substrate being servers that support Network FunctionVirtualization (NFV), followed by packet forwarding capabilities basedon generic computing resources, with specialized network technologiesbrought to bear when general purpose processors or general purposeintegrated circuit devices offered by merchants (referred to herein asmerchant silicon) are not appropriate. In this case, communicationservices can be implemented as cloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1 ),such as an edge router can be implemented via a VNE 330 composed of NFVsoftware modules, merchant silicon, and associated controllers. Thesoftware can be written so that increasing workload consumes incrementalresources from a common resource pool, and moreover so that it'selastic: so the resources are only consumed when needed. In a similarfashion, other network elements such as other routers, switches, edgecaches, and middle-boxes are instantiated from the common resource pool.Such sharing of infrastructure across a broad set of uses makes planningand growing infrastructure easier to manage.

In an embodiment, the transport layer 350 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless access 120, voice access 130,media access 140 and/or access to content sources 175 for distributionof content to any or all of the access technologies. In particular, insome cases a network element needs to be positioned at a specific place,and this allows for less sharing of common infrastructure. Other times,the network elements have specific physical layer adapters that cannotbe abstracted or virtualized, and might require special DSP code andanalog front-ends (AFEs) that do not lend themselves to implementationas VNEs 330, 332 or 334. These network elements can be included intransport layer 350.

The virtualized network function cloud 325 interfaces with the transportlayer 350 to provide the VNEs 330, 332, 334, etc. to provide specificNFVs. In particular, the virtualized network function cloud 325leverages cloud operations, applications, and architectures to supportnetworking workloads. The virtualized network elements 330, 332 and 334can employ network function software that provides either a one-for-onemapping of traditional network element function or alternately somecombination of network functions designed for cloud computing. Forexample, VNEs 330, 332 and 334 can include route reflectors, domain namesystem (DNS) servers, and dynamic host configuration protocol (DHCP)servers, system architecture evolution (SAE) and/or mobility managemententity (MME) gateways, broadband network gateways, IP edge routers forIP-VPN, Ethernet and other services, load balancers, distributers andother network elements. Because these elements don't typically need toforward large amounts of traffic, their workload can be distributedacross a number of servers—each of which adds a portion of thecapability, and overall which creates an elastic function with higheravailability than its former monolithic version. These virtual networkelements 330, 332, 334, etc. can be instantiated and managed using anorchestration approach similar to those used in cloud compute services.

The cloud computing environments 375 can interface with the virtualizednetwork function cloud 325 via APIs that expose functional capabilitiesof the VNEs 330, 332, 334, etc. to provide the flexible and expandedcapabilities to the virtualized network function cloud 325. Inparticular, network workloads may have applications distributed acrossthe virtualized network function cloud 325 and cloud computingenvironment 375 and in the commercial cloud, or might simply orchestrateworkloads supported entirely in NFV infrastructure from these thirdparty locations.

Turning now to FIG. 4 , there is illustrated a block diagram of acomputing environment in accordance with various aspects describedherein. In order to provide additional context for various embodimentsof the embodiments described herein, FIG. 4 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 400 in which the various embodiments of thesubject disclosure can be implemented. In particular, computingenvironment 400 can be used in the implementation of network elements150, 152, 154, 156, access terminal 112, base station or access point122, switching device 132, media terminal 142, and/or VNEs 330, 332,334, etc. Each of these devices can be implemented viacomputer-executable instructions that can run on one or more computers,and/or in combination with other program modules and/or as a combinationof hardware and software. For example, computing environment 400 canfacilitate in whole or in part panoramic video streaming (such as in thecontext of viewport prediction/selection as described herein).

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors aswell as other application specific circuits such as an applicationspecific integrated circuit, digital logic circuit, state machine,programmable gate array or other circuit that processes input signals ordata and that produces output signals or data in response thereto. Itshould be noted that while any functions and features described hereinin association with the operation of a processor could likewise beperformed by a processing circuit.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can comprise, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

With reference again to FIG. 4 , the example environment can comprise acomputer 402, the computer 402 comprising a processing unit 404, asystem memory 406 and a system bus 408. The system bus 408 couplessystem components including, but not limited to, the system memory 406to the processing unit 404. The processing unit 404 can be any ofvarious commercially available processors. Dual microprocessors andother multiprocessor architectures can also be employed as theprocessing unit 404.

The system bus 408 can be any of several types of bus structure that canfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 406comprises ROM 410 and RAM 412. A basic input/output system (BIOS) can bestored in a non-volatile memory such as ROM, erasable programmable readonly memory (EPROM), EEPROM, which BIOS contains the basic routines thathelp to transfer information between elements within the computer 402,such as during startup. The RAM 412 can also comprise a high-speed RAMsuch as static RAM for caching data.

The computer 402 further comprises an internal hard disk drive (HDD) 414(e.g., EIDE, SATA), which internal HDD 414 can also be configured forexternal use in a suitable chassis (not shown), a magnetic floppy diskdrive (FDD) 416, (e.g., to read from or write to a removable diskette418) and an optical disk drive 420, (e.g., reading a CD-ROM disk 422 or,to read from or write to other high capacity optical media such as theDVD). The HDD 414, magnetic FDD 416 and optical disk drive 420 can beconnected to the system bus 408 by a hard disk drive interface 424, amagnetic disk drive interface 426 and an optical drive interface 428,respectively. The hard disk drive interface 424 for external driveimplementations comprises at least one or both of Universal Serial Bus(USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394interface technologies. Other external drive connection technologies arewithin contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 402, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto a hard disk drive (HDD), a removable magnetic diskette, and aremovable optical media such as a CD or DVD, it should be appreciated bythose skilled in the art that other types of storage media which arereadable by a computer, such as zip drives, magnetic cassettes, flashmemory cards, cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methodsdescribed herein.

A number of program modules can be stored in the drives and RAM 412,comprising an operating system 430, one or more application programs432, other program modules 434 and program data 436. All or portions ofthe operating system, applications, modules, and/or data can also becached in the RAM 412. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 402 throughone or more wired/wireless input devices, e.g., a keyboard 438 and apointing device, such as a mouse 440. Other input devices (not shown)can comprise a microphone, an infrared (IR) remote control, a joystick,a game pad, a stylus pen, touch screen or the like. These and otherinput devices are often connected to the processing unit 404 through aninput device interface 442 that can be coupled to the system bus 408,but can be connected by other interfaces, such as a parallel port, anIEEE 1394 serial port, a game port, a universal serial bus (USB) port,an IR interface, etc.

A monitor 444 or other type of display device can be also connected tothe system bus 408 via an interface, such as a video adapter 446. Itwill also be appreciated that in alternative embodiments, a monitor 444can also be any display device (e.g., another computer having a display,a smart phone, a tablet computer, etc.) for receiving displayinformation associated with computer 402 via any communication means,including via the Internet and cloud-based networks. In addition to themonitor 444, a computer typically comprises other peripheral outputdevices (not shown), such as speakers, printers, etc.

The computer 402 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 448. The remotecomputer(s) 448 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer402, although, for purposes of brevity, only a remote memory/storagedevice 450 is illustrated. The logical connections depicted comprisewired/wireless connectivity to a local area network (LAN) 452 and/orlarger networks, e.g., a wide area network (WAN) 454. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 402 can beconnected to the LAN 452 through a wired and/or wireless communicationnetwork interface or adapter 456. The adapter 456 can facilitate wiredor wireless communication to the LAN 452, which can also comprise awireless AP disposed thereon for communicating with the adapter 456.

When used in a WAN networking environment, the computer 402 can comprisea modem 458 or can be connected to a communications server on the WAN454 or has other means for establishing communications over the WAN 454,such as by way of the Internet. The modem 458, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 408 via the input device interface 442. In a networked environment,program modules depicted relative to the computer 402 or portionsthereof, can be stored in the remote memory/storage device 450. It willbe appreciated that the network connections shown are example and othermeans of establishing a communications link between the computers can beused.

The computer 402 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can comprise WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands for example or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10BaseT wired Ethernetnetworks used in many offices.

Turning now to FIG. 5 , an embodiment 500 of a mobile network platform510 is shown that is an example of network elements 150, 152, 154, 156,and/or VNEs 330, 332, 334, etc. For example, platform 510 can facilitatein whole or in part panoramic video streaming (such as in the context ofviewport prediction/selection as described herein). In one or moreembodiments, the mobile network platform 510 can generate and receivesignals transmitted and received by base stations or access points suchas base station or access point 122. Generally, mobile network platform510 can comprise components, e.g., nodes, gateways, interfaces, servers,or disparate platforms, that facilitate both packet-switched (PS) (e.g.,internet protocol (IP), frame relay, asynchronous transfer mode (ATM))and circuit-switched (CS) traffic (e.g., voice and data), as well ascontrol generation for networked wireless telecommunication. As anon-limiting example, mobile network platform 510 can be included intelecommunications carrier networks, and can be considered carrier-sidecomponents as discussed elsewhere herein. Mobile network platform 510comprises CS gateway node(s) 512 which can interface CS traffic receivedfrom legacy networks like telephony network(s) 540 (e.g., publicswitched telephone network (PSTN), or public land mobile network (PLMN))or a signaling system #7 (SS7) network 560. CS gateway node(s) 512 canauthorize and authenticate traffic (e.g., voice) arising from suchnetworks. Additionally, CS gateway node(s) 512 can access mobility, orroaming, data generated through SS7 network 560; for instance, mobilitydata stored in a visited location register (VLR), which can reside inmemory 530. Moreover, CS gateway node(s) 512 interfaces CS-based trafficand signaling and PS gateway node(s) 518. As an example, in a 3GPP UMTSnetwork, CS gateway node(s) 512 can be realized at least in part ingateway GPRS support node(s) (GGSN). It should be appreciated thatfunctionality and specific operation of CS gateway node(s) 512, PSgateway node(s) 518, and serving node(s) 516, is provided and dictatedby radio technology(ies) utilized by mobile network platform 510 fortelecommunication over a radio access network 520 with other devices,such as a radiotelephone 575.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 518 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions cancomprise traffic, or content(s), exchanged with networks external to themobile network platform 510, like wide area network(s) (WANs) 550,enterprise network(s) 570, and service network(s) 580, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 510 through PS gateway node(s) 518. It is to benoted that WANs 550 and enterprise network(s) 570 can embody, at leastin part, a service network(s) like IP multimedia subsystem (IMS). Basedon radio technology layer(s) available in technology resource(s) orradio access network 520, PS gateway node(s) 518 can generate packetdata protocol contexts when a data session is established; other datastructures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 518 cancomprise a tunnel interface (e.g., tunnel termination gateway (TTG) in3GPP UMTS network(s) (not shown)) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks.

In embodiment 500, mobile network platform 510 also comprises servingnode(s) 516 that, based upon available radio technology layer(s) withintechnology resource(s) in the radio access network 520, convey thevarious packetized flows of data streams received through PS gatewaynode(s) 518. It is to be noted that for technology resource(s) that relyprimarily on CS communication, server node(s) can deliver trafficwithout reliance on PS gateway node(s) 518; for example, server node(s)can embody at least in part a mobile switching center. As an example, ina 3GPP UMTS network, serving node(s) 516 can be embodied in serving GPRSsupport node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)514 in mobile network platform 510 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can comprise add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bymobile network platform 510. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 518 for authorization/authentication and initiation of a datasession, and to serving node(s) 516 for communication thereafter. Inaddition to application server, server(s) 514 can comprise utilityserver(s), a utility server can comprise a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through mobile network platform 510 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 512and PS gateway node(s) 518 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 550 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to mobilenetwork platform 510 (e.g., deployed and operated by the same serviceprovider), such as the distributed antennas networks shown in FIG. 1(s)that enhance wireless service coverage by providing more networkcoverage.

It is to be noted that server(s) 514 can comprise one or more processorsconfigured to confer at least in part the functionality of mobilenetwork platform 510. To that end, the one or more processor can executecode instructions stored in memory 530, for example. It is should beappreciated that server(s) 514 can comprise a content manager, whichoperates in substantially the same manner as described hereinbefore.

In example embodiment 500, memory 530 can store information related tooperation of mobile network platform 510. Other operational informationcan comprise provisioning information of mobile devices served throughmobile network platform 510, subscriber databases; applicationintelligence, pricing schemes, e.g., promotional rates, flat-rateprograms, couponing campaigns; technical specification(s) consistentwith telecommunication protocols for operation of disparate radio, orwireless, technology layers; and so forth. Memory 530 can also storeinformation from at least one of telephony network(s) 540, WAN 550, SS7network 560, or enterprise network(s) 570. In an aspect, memory 530 canbe, for example, accessed as part of a data store component or as aremotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 5 , and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that perform particulartasks and/or implement particular abstract data types.

Turning now to FIG. 6 , an illustrative embodiment of a communicationdevice 600 is shown. The communication device 600 can serve as anillustrative embodiment of devices such as data terminals 114, mobiledevices 124, vehicle 126, display devices 144 or other client devicesfor communication via either communications network 125. For example,computing device 600 can facilitate in whole or in part panoramic videostreaming (such as in the context of viewport prediction/selection asdescribed herein).

The communication device 600 can comprise a wireline and/or wirelesstransceiver 602 (herein transceiver 602), a user interface (UI) 604, apower supply 614, a location receiver 616, a motion sensor 618, anorientation sensor 620, and a controller 606 for managing operationsthereof. The transceiver 602 can support short-range or long-rangewireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, orcellular communication technologies, just to mention a few (Bluetooth®and ZigBee® are trademarks registered by the Bluetooth® Special InterestGroup and the ZigBee® Alliance, respectively). Cellular technologies caninclude, for example, CDMA-1X, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO,WiMAX, SDR, LTE, as well as other next generation wireless communicationtechnologies as they arise. The transceiver 602 can also be adapted tosupport circuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof.

The UI 604 can include a depressible or touch-sensitive keypad 608 witha navigation mechanism such as a roller ball, a joystick, a mouse, or anavigation disk for manipulating operations of the communication device600. The keypad 608 can be an integral part of a housing assembly of thecommunication device 600 or an independent device operably coupledthereto by a tethered wireline interface (such as a USB cable) or awireless interface supporting for example Bluetooth®. The keypad 608 canrepresent a numeric keypad commonly used by phones, and/or a QWERTYkeypad with alphanumeric keys. The UI 604 can further include a display610 such as monochrome or color LCD (Liquid Crystal Display), OLED(Organic Light Emitting Diode) or other suitable display technology forconveying images to an end user of the communication device 600. In anembodiment where the display 610 is touch-sensitive, a portion or all ofthe keypad 608 can be presented by way of the display 610 withnavigation features.

The display 610 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 600 can be adapted to present a user interfacehaving graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The display 610 can be equipped withcapacitive, resistive or other forms of sensing technology to detect howmuch surface area of a user's finger has been placed on a portion of thetouch screen display. This sensing information can be used to controlthe manipulation of the GUI elements or other functions of the userinterface. The display 610 can be an integral part of the housingassembly of the communication device 600 or an independent devicecommunicatively coupled thereto by a tethered wireline interface (suchas a cable) or a wireless interface.

The UI 604 can also include an audio system 612 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high volume audio (such as speakerphonefor hands free operation). The audio system 612 can further include amicrophone for receiving audible signals of an end user. The audiosystem 612 can also be used for voice recognition applications. The UI604 can further include an image sensor 613 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 614 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 600 to facilitatelong-range or short-range portable communications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 616 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 600 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 618can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 600 in three-dimensional space. Theorientation sensor 620 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device600 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 600 can use the transceiver 602 to alsodetermine a proximity to a cellular, WiFi, Bluetooth®, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 606 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 600.

Other components not shown in FIG. 6 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 600 can include a slot for adding or removing an identity modulesuch as a Subscriber Identity Module (SIM) card or Universal IntegratedCircuit Card (UICC). SIM or UICC cards can be used for identifyingsubscriber services, executing programs, storing subscriber data, and soon.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory, non-volatile memory, disk storage, and memory storage. Further,nonvolatile memory can be included in read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory cancomprise random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).Additionally, the disclosed memory components of systems or methodsherein are intended to comprise, without being limited to comprising,these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, smartphone, watch, tabletcomputers, netbook computers, etc.), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network; however, some if not allaspects of the subject disclosure can be practiced on stand-alonecomputers. In a distributed computing environment, program modules canbe located in both local and remote memory storage devices.

In one or more embodiments, information regarding use of services can begenerated including services being accessed, media consumption history,user preferences, and so forth. This information can be obtained byvarious methods including user input, detecting types of communications(e.g., video content vs. audio content), analysis of content streams,sampling, and so forth. The generating, obtaining and/or monitoring ofthis information can be responsive to an authorization provided by theuser. In one or more embodiments, an analysis of data can be subject toauthorization from user(s) associated with the data, such as an opt-in,an opt-out, acknowledgement requirements, notifications, selectiveauthorization based on types of data, and so forth.

Some of the embodiments described herein can also employ artificialintelligence (AI) to facilitate automating one or more featuresdescribed herein. The embodiments (e.g., in connection withautomatically predicting and/or selecting one or more viewports for usein performing panoramic video streaming) can employ various AI-basedschemes for carrying out various embodiments thereof. Moreover, theclassifier can be employed to determine a ranking or priority of eachcell site of the acquired network. A classifier is a function that mapsan input attribute vector, x=(x1, x2, x3, x4, . . . , xn), to aconfidence that the input belongs to a class, that is, f(x)=confidence(class). Such classification can employ a probabilistic and/orstatistical-based analysis (e.g., factoring into the analysis utilitiesand costs) to determine or infer an action that a user desires to beautomatically performed. A support vector machine (SVM) is an example ofa classifier that can be employed. The SVM operates by finding ahypersurface in the space of possible inputs, which the hypersurfaceattempts to split the triggering criteria from the non-triggeringevents. Intuitively, this makes the classification correct for testingdata that is near, but not identical to training data. Other directedand undirected model classification approaches comprise, e.g., naïveBayes, Bayesian networks, decision trees, neural networks, fuzzy logicmodels, and probabilistic classification models providing differentpatterns of independence can be employed. Classification as used hereinalso is inclusive of statistical regression that is utilized to developmodels of priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to predetermined criteria which of the acquiredcell sites will benefit a maximum number of subscribers and/or which ofthe acquired cell sites will add minimum value to the existingcommunication network coverage, etc.

As used in some contexts in this application, in some embodiments, theterms “component,” “system” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution,computer-executable instructions, a program, and/or a computer. By wayof illustration and not limitation, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. While various components have beenillustrated as separate components, it will be appreciated that multiplecomponents can be implemented as a single component, or a singlecomponent can be implemented as multiple components, without departingfrom example embodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based, at least, on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,”and substantially any other information storage component relevant tooperation and functionality of a component, refer to “memorycomponents,” or entities embodied in a “memory” or components comprisingthe memory. It will be appreciated that the memory components orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory or can include both volatile andnonvolatile memory.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

As may also be used herein, the term(s) “operably coupled to”, “coupledto”, and/or “coupling” includes direct coupling between items and/orindirect coupling between items via one or more intervening items. Suchitems and intervening items include, but are not limited to, junctions,communication paths, components, circuit elements, circuits, functionalblocks, and/or devices. As an example of indirect coupling, a signalconveyed from a first item to a second item may be modified by one ormore intervening items by modifying the form, nature or format ofinformation in a signal, while one or more elements of the informationin the signal are nevertheless conveyed in a manner than can berecognized by the second item. In a further example of indirectcoupling, an action in a first item can cause a reaction on the seconditem, as a result of actions and/or reactions in one or more interveningitems.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover any and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all of the features described with respect to anembodiment can also be utilized.

What is claimed is:
 1. A device comprising: a processing systemincluding a processor; and a memory that stores executable instructionsthat, when executed by the processing system, facilitate performance ofoperations, the operations comprising: obtaining first head movementdata associated with media content from a database of historic headmovement data, wherein the historic head movement data are obtained froma group of previous communication sessions that viewed the mediacontent; training a machine learning application based on the first headmovement data; obtaining second head movement data associated withviewing the media content; determining a viewport associated with themedia content based on the first head movement data and the second headmovement data utilizing the machine learning application resulting in afirst determination; and providing a portion of the media contentassociated with the viewport to a communication device based on thefirst determination.
 2. The device of claim 1, wherein the operationscomprise obtaining a group of signals, wherein the group of signals isbased on a second determination of brain activity of a viewer viewingmedia content.
 3. The device of claim 2, wherein the determining of theviewport comprises determining the viewport associated with the mediacontent based on the brain activity.
 4. The device of claim 2, whereinthe group of signals is obtained in real-time via electroencephalography(EEG), magnetoencephalography (MEG), functional Magnetic ResonanceImaging (fMRI), or any combination thereof.
 5. The device of claim 1,wherein the obtaining of the second head movement data comprisesdetermining a first portion of the second head movement data associatedwith the media content from a motion sensor.
 6. The device of claim 1,wherein a second portion of the second head movement data is associatedwith a viewer.
 7. The device of claim 1, wherein a third portion of thesecond head movement data is obtained in real-time while a viewer isviewing the media content.
 8. The device of claim 1, wherein the mediacontent comprises panoramic media content.
 9. A non-transitorymachine-readable medium comprising executable instructions that, whenexecuted by a processing system, facilitate performance of operations,the operations comprising: obtaining first head movement data associatedwith media content from a database of historic head movement data,wherein the historic head movement data are obtained from a group ofprevious communication sessions that viewed the media content; traininga machine learning application based on the first head movement data;obtaining a group of signals, wherein the group of signals is based on afirst determination of brain activity of a viewer viewing media content.determining second head movement data associated with viewing the mediacontent based on the first determination; determining a viewportassociated with the media content based on the first head movement dataand the second head movement data utilizing the machine learningapplication resulting in a second determination; and providing a portionof the media content associated with the viewport to a communicationdevice based on the second determination.
 10. The non-transitorymachine-readable medium of claim 9, wherein the group of signals isobtained in real-time via electroencephalography (EEG),magnetoencephalography (MEG), functional Magnetic Resonance Imaging(fMRI), or any combination thereof.
 11. The non-transitorymachine-readable medium of claim 9, wherein the determining of thesecond head movement data comprises obtaining a motion informationassociated with a first portion of the second head movement dataassociated with the media content from a motion sensor.
 12. Thenon-transitory machine-readable medium of claim 11, wherein thedetermining of the second head movement data comprises determining thefirst portion of the second head movement data based on the motioninformation.
 13. The non-transitory machine-readable medium of claim 9,wherein a second portion of the second head movement data is associatedwith the viewer.
 14. The non-transitory machine-readable medium of claim9, wherein a third portion of the second head movement data is obtainedin real-time while the viewer is viewing the media content.
 15. Thenon-transitory machine-readable medium of claim 9, wherein the mediacontent comprises panoramic media content.
 16. A method, comprising:obtaining, by a processing system including a processor, first headmovement data associated with panoramic media content from a database ofhistoric head movement data, wherein the historic head movement data areobtained from a group of previous communication sessions that viewed thepanoramic media content; training, by the processing system, a machinelearning application based on the first head movement data; obtaining,by the processing system, second head movement data associated withviewing the panoramic media content; determining, by the processingsystem, a viewport associated with the panoramic media content based onthe first head movement data and the second head movement data utilizingthe machine learning application resulting in a first determination; andproviding, by the processing system, a portion of the panoramic mediacontent associated with the viewport to a communication device based onthe first determination.
 17. The method of claim 16, comprisingobtaining, by the processing system, a group of signals, wherein thegroup of signals is based on a second determination of brain activity ofa viewer viewing panoramic media content.
 18. The method of claim 17,wherein the determining of the viewport comprises determining, by theprocessing system, the viewport associated with the panoramic mediacontent based on the brain activity.
 19. The method of claim 17, whereinthe group of signals is obtained in real-time via electroencephalography(EEG), magnetoencephalography (MEG), functional Magnetic ResonanceImaging (fMRI), or any combination thereof.
 20. The method of claim 16,wherein the obtaining of the second head movement data comprisesdetermining, by the processing system, a portion of the second headmovement data associated with the panoramic media content from a motionsensor.